Analysis of walking times to nearest schools across the ACT.
Population-weighted average walk time
Change: +3.1 mins vs 2001
Population-weighted average walk time
Change: +2.5 mins vs 2001
---
title: "Average Walk Time Analysis"
---
Analysis of walking times to nearest schools across the ACT.
```{r setup}
#| echo: false
#| message: false
#| warning: false
library(sf)
library(leaflet)
library(dplyr)
library(ggplot2)
library(plotly)
library(htmltools)
# Load walk time data
walk_2001 <- st_read(here::here("data", "walk_analysis", "school_walk_times_2001.gpkg"), quiet = TRUE)
walk_2024 <- st_read(here::here("data", "walk_analysis", "school_walk_times_2024.gpkg"), quiet = TRUE)
primary_2001 <- walk_2001 |> filter(school_type == "primary")
primary_2024 <- walk_2024 |> filter(school_type == "primary")
high_2001 <- walk_2001 |> filter(school_type == "high")
high_2024 <- walk_2024 |> filter(school_type == "high")
```
```{r}
#| echo: false
# Create KPI callout function
create_kpi_callout <- function(title, value, subtitle, color = "#2166AC") {
div(
style = paste0("background-color: ", color, "; color: white; padding: 20px;
border-radius: 8px; text-align: center; margin: 10px;"),
h3(title, style = "margin: 0; font-size: 1.2em;"),
h1(paste0(value, " mins"), style = "margin: 5px 0; font-size: 2.5em; font-weight: bold;"),
p(subtitle, style = "margin: 0; font-size: 0.9em; opacity: 0.9;")
)
}
# Get specific stats for KPIs
primary_2001_avg <- round(sum(primary_2001$mean_walk_time_min *
primary_2001$population) / sum(primary_2001$population), digits = 1)
primary_2024_avg <- round(sum(primary_2024$mean_walk_time_min *
primary_2024$population) / sum(primary_2024$population), digits = 1)
high_2001_avg <- round(sum(high_2001$mean_walk_time_min *
high_2001$population) / sum(high_2001$population), digits = 1)
high_2024_avg <- round(sum(high_2024$mean_walk_time_min *
high_2024$population) / sum(high_2024$population), digits = 1)
```
# Primary Schools
## Walk Time KPIs
::: {.columns}
::: {.column width="50%"}
```{r}
#| echo: false
# Create 2001 Primary KPI
create_kpi_callout(
title = "Primary Schools 2001",
value = primary_2001_avg,
subtitle = "Population-weighted average walk time",
color = "#1e3a8a"
)
```
:::
::: {.column width="50%"}
```{r}
#| echo: false
# Calculate change for color coding
primary_change <- primary_2024_avg - primary_2001_avg
change_color <- if(primary_change > 0) "#dc2626" else "#16a34a" # Red if worse, green if better
# Create 2024 Primary KPI with change info
create_kpi_callout(
title = "Primary Schools 2024",
value = primary_2024_avg,
subtitle = paste0("Change: ", ifelse(primary_change >= 0, "+", ""), round(primary_change, 1), " mins vs 2001"),
color = change_color
)
```
:::
:::
## Primary School Walk Time Maps
### 2024
```{r}
create_walk_time_map <- function(data, year, school_type) {
# Calculate color palette - using plasma reversed so shorter times are yellow/bright
pal <- colorNumeric("plasma", data$mean_walk_time_min, na.color = "#cccccc", reverse = TRUE)
leaflet(data) |>
addProviderTiles(providers$CartoDB.Positron) |>
addPolygons(
fillColor = ~pal(mean_walk_time_min),
fillOpacity = ~ifelse(population == 0, 0, 0.7), # Transparent if no population
color = "#444444",
weight = 1,
label = ~paste0(
sa2_name, "<br>",
"Walk Time: ", round(mean_walk_time_min, 1), " mins<br>",
"Population (5-14): ", population, "<br>",
"Year: ", year
) |> lapply(htmltools::HTML),
labelOptions = labelOptions(sticky = TRUE)
) |>
addLegend(
pal = pal,
values = ~mean_walk_time_min,
title = paste("Walk Time (mins) <br>", school_type, year),
position = "bottomright"
) |>
setView(lng = 149.12, lat = -35.30, zoom = 10)
}
create_walk_time_map(primary_2024, 2024, "Primary")
```
### 2001
```{r}
#| echo: false
create_walk_time_map(primary_2001, 2001, "Primary")
```
# High Schools
## Walk Time KPIs
::: {.columns}
::: {.column width="50%"}
```{r}
#| echo: false
# Create 2001 Primary KPI
create_kpi_callout(
title = "High Schools 2001",
value = high_2001_avg,
subtitle = "Population-weighted average walk time",
color = "#1e3a8a"
)
```
:::
::: {.column width="50%"}
```{r}
#| echo: false
# Calculate change for color coding
high_change <- high_2024_avg - high_2001_avg
change_color <- if(high_change > 0) "#dc2626" else "#16a34a" # Red if worse, green if better
# Create 2024 high KPI with change info
create_kpi_callout(
title = "Primary Schools 2024",
value = high_2024_avg,
subtitle = paste0("Change: ", ifelse(high_change >= 0, "+", ""), round(high_change, 1), " mins vs 2001"),
color = change_color
)
```
:::
:::
## High School Walk Time Maps
### 2024
```{r}
create_walk_time_map(high_2024, 2024, "High")
```
### 2001
```{r}
#| echo: false
create_walk_time_map(high_2001, 2001, "High")
```